Disturbance Accommodating Model Predictive Control

Unmanned aerial systems (UAS) operating in military environments are subjected to harsh conditions due to weather, adversaries, and busy environments. Control systems must be capable of operating UAS safely and effectively in this environment in the presence of abrupt disturbances. Integrating additional sensor modalities (such as computer vision, advanced inertial measurement, atmospheric sensing, etc.) into the control system feedback loop could enable effective UAS operation in contested environments. These sensors may provide more data than is typically used in current flight control systems, and control algorithms must be capable of handling this information in a computationally tractable manner. We will combine the ability to observe upcoming air conditions with a controller based on robust sampling-based stochastic model predictive control. This approach will enable UAS to operate robustlyĀ in contested, adversarial environments.